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Curricular information is subject to change
Upon completion of the module, students should be able to:
1. Identify suitable machine learning / deep learning approaches given a specific task / application.
2. Design and implement data analytics pipeline using Python libraries using industry adopted platforms.
3. Critically evaluate the performance of machine learning / deep learning models.
a) Medical imaging analysis (Deep Learning for computer vision with various image types)
b) Multi-Omics - e.g., gene expression inference, analysis of human splicing codes - determination of disease, prediction of non-coding variant, etc.
c) Protein structure prediction - e.g., prediction of protein secondary structure, prediction of protein contact map, etc.
At the end of the module students will be able to implement the various techniques of ML/DL on the specific application themes such as those mentioned above. Students will explore the latest state-of-the-art models running on commercial platforms (e.g., TensorFlow on google).
|Student Effort Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
Some prior experience with programming in Python and working with the object-oriented programming paradigm.
Completion of the Module MEIN40310 - Python for the Life Sciences
|Description||Timing||Component Scale||% of Final Grade|
|Project: Two components (Project 1 - 50% Wk 1 to 6, and Project 2 - 50% Wk 7 to 12), where students will apply ML or DL modelling on two life science problems.||Throughout the Trimester||n/a||Standard conversion grade scale 40%||Yes||
|Resit In||Terminal Exam|
• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
|Professor Brendan Loftus||Tutor|